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Detecting Deepfakes and Forged Videos Using Deep Learning

Johansson, Emil LU (2020) In Master's Theses in Mathematical Sciences FMAM05 20201
Mathematics (Faculty of Engineering)
Abstract
Over just a few years, methods to manipulate videos have become so sophistica- ted that even someone without much expertise or computational resources can forge videos inseparable from pristine ones to the human eye. These methods can for instance insert a person in a video or manipulate their lip movements to make them say anything of the manipulator’s liking. Though there exist harm- less and constructive uses of these technologies, it is not hard to imagine the harm they could cause if put in the wrong hands.
This report presents a model to detect forged manipulated videos, more specifically those where faces have been manipulated. Four kinds of manipu- lation videos were taken into consideration: FaceSwap, DeepFakes, Face2Face and... (More)
Over just a few years, methods to manipulate videos have become so sophistica- ted that even someone without much expertise or computational resources can forge videos inseparable from pristine ones to the human eye. These methods can for instance insert a person in a video or manipulate their lip movements to make them say anything of the manipulator’s liking. Though there exist harm- less and constructive uses of these technologies, it is not hard to imagine the harm they could cause if put in the wrong hands.
This report presents a model to detect forged manipulated videos, more specifically those where faces have been manipulated. Four kinds of manipu- lation videos were taken into consideration: FaceSwap, DeepFakes, Face2Face and Neural Textures. The model proposed consists of a feature extraction CNN followed by an LSTM network. The FaceForensics++ dataset was used, as well as the associated benchmark. The model, though not competing with the state- of-the-art detectors, was able to classify videos with an accuracy higher than or close to that of several models in the benchmark. (Less)
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author
Johansson, Emil LU
supervisor
organization
alternative title
Detektering av Deepfakes och förfalskade videor med hjälp av djupinlärning
course
FMAM05 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Image Analysis, Deepfake, Deep Learning, Neural Networks, FaceSwap, Face2Face, Neural Textures
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3414-2020
ISSN
1404-6342
other publication id
2020:E34
language
English
id
9019746
date added to LUP
2020-06-23 13:17:38
date last changed
2020-06-23 13:17:38
@misc{9019746,
  abstract     = {Over just a few years, methods to manipulate videos have become so sophistica- ted that even someone without much expertise or computational resources can forge videos inseparable from pristine ones to the human eye. These methods can for instance insert a person in a video or manipulate their lip movements to make them say anything of the manipulator’s liking. Though there exist harm- less and constructive uses of these technologies, it is not hard to imagine the harm they could cause if put in the wrong hands.
This report presents a model to detect forged manipulated videos, more specifically those where faces have been manipulated. Four kinds of manipu- lation videos were taken into consideration: FaceSwap, DeepFakes, Face2Face and Neural Textures. The model proposed consists of a feature extraction CNN followed by an LSTM network. The FaceForensics++ dataset was used, as well as the associated benchmark. The model, though not competing with the state- of-the-art detectors, was able to classify videos with an accuracy higher than or close to that of several models in the benchmark.},
  author       = {Johansson, Emil},
  issn         = {1404-6342},
  keyword      = {Image Analysis,Deepfake,Deep Learning,Neural Networks,FaceSwap,Face2Face,Neural Textures},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Detecting Deepfakes and Forged Videos Using Deep Learning},
  year         = {2020},
}